Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.Comment: Submitted to https://link.springer.com/chapter/10.1007/978-3-030-52791-4_

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types

Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study

Background: Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists’ subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM. Methods: We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed. Results: The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion's silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information. Conclusion: In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level

Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin.

Background: Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage.Methods: A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm's robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods.Results: Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage.Conclusions: Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues

Inhibition of DNA damage response at telomeres improves the detrimental phenotypes of Hutchinson–Gilford Progeria Syndrome

Hutchinson–Gilford progeria syndrome (HGPS) is a genetic disorder characterized by premature aging features. Cells from HGPS patients express progerin, a truncated form of Lamin A, which perturbs cellular homeostasis leading to nuclear shape alterations, genome instability, heterochromatin loss, telomere dysfunction and premature entry into cellular senescence. Recently, we reported that telomere dysfunction induces the transcription of telomeric non-coding RNAs (tncRNAs) which control the DNA damage response (DDR) at dysfunctional telomeres. Here we show that progerin-induced telomere dysfunction induces the transcription of tncRNAs. Their functional inhibition by sequence-specific telomeric antisense oligonucleotides (tASOs) prevents full DDR activation and premature cellular senescence in various HGPS cell systems, including HGPS patient fibroblasts. We also show in vivo that tASO treatment significantly enhances skin homeostasis and lifespan in a transgenic HGPS mouse model. In summary, our results demonstrate an important role for telomeric DDR activation in HGPS progeroid detrimental phenotypes in vitro and in vivo

Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology

The spectacular response observed in clinical trials of immunotherapy in patients with previously uncurable Melanoma, a highly aggressive form of skin cancer, calls for a better understanding of the cancer-immune interface. Computational pathology provides a unique opportunity to spatially dissect such interface on digitised pathological slides. Accurate cellular classification is a key to ensure meaningful results, but is often challenging even with state-of-art machine learning and deep learning methods. We propose a hierarchical framework, which mirrors the way pathologists perceive tumour architecture and define tumour heterogeneity to improve cell classification methods that rely solely on cell nuclei morphology. The SLIC superpixel algorithm was used to segment and classify tumour regions in low resolution H&E-stained histological images of melanoma skin cancer to provide a global context. Classification of superpixels into tumour, stroma, epidermis and lumen/white space, yielded a 97.7% training set accuracy and 95.7% testing set accuracy in 58 whole-tumour images of the TCGA melanoma dataset. The superpixel classification was projected down to high resolution images to enhance the performance of a single cell classifier, based on cell nuclear morphological features, and resulted in increasing its accuracy from 86.4% to 91.6%. Furthermore, a voting scheme was proposed to use global context as biological a priori knowledge, pushing the accuracy further to 92.8%. This study demonstrates how using the global spatial context can accurately characterise the tumour microenvironment and allow us to extend significantly beyond single-cell morphological classification.Comment: Accepted by MICCAI COMPAY 2018 worksho

Automated characterization of Tumor-Infiltrating Lymphocytes (TIL) in histological breast images

Cancer illness has a big influence on society. Its extended proliferation and high aggressiveness make it a difficult problem to solve and therefore a big deal for science. Recently, a research trend has been focusing on how 3D tumor structure affects the development of the cancer and its outcome, especially metastasis. Stromal structure and tumor cell signaling are processes that highly influence tumor migration. Thus, histological analysis becomes a fundamental tool to study tumor structure, which provides valuable information about cell characteristics and organization. The relevance of histological study is supported by the increasing interest of anatomopathologists to have good automatic solutions to support the specialist’s diagnosis. For this purpose, the current thesis proposes an automated approach to analyze hematoxylin and eosin (H&E) stained histological images, particularly coming from breast cancer patients. The proposed method consists on the classification of the nuclei in H&E-stained histological images and the further analysis of tumor-infiltrating lymphocytes (TIL) present on the visualized section. The starting point of the approach is the automatic nuclei-segmented binary mask. Each of the segmented nuclei is classified into two types, cancerous or healthy. The classification is performed by a trained artificial neural network to give two binary masks, each of them containing one type of nuclei. Then, the algorithm can follow two different paths: classification of zones or TIL analysis. Classification of zones has the aim to provide a more comfortable support to perform cancer diagnosis, because it provides quantitative information of tumor lobule size. To achieve it, a nuclei correction step is executed, by which each nucleus class depends on the area surrounding it. In this way, a clearer vision of the existing zones is provided (tumor lobule or tumor microenvironment). The other approach is to perform TIL analysis. This technique is based on the nuclei classified binary masks and analyzes the immune system response against the tumor. This way, healthy cells of tumor microenvironment are detected and quantified. The ratio of TIL occupied area to free microenvironment area is computed as informational parameter. This ratio is calculated by the combination of a manually-segmented zone binary mask and the nuclei classified binary mask. In this way, only healthy nuclei of microenvironment zone are considered, dividing the sum of their area by the free sections of the microenvironment zone (i.e. area of microenvironment zone where nuclei are not present). Moreover, the TIL dispersion factor is computed to study their distribution throughout the area by dividing the microenvironment area in several zones and calculate the standard deviation of the area of lymphocytes within each of them. Afterward, the opposed of standard deviation is computed to obtain the dispersion factor. Automatic results are found to match the gold standard (the pathologist’s diagnosis), although some error is observed after evaluation. The approach taken in this work has a positive outlook, even though some aspects need to be polished, like the algorithm accuracy and the use of a larger set of images to claim a proper functionality for global cases.Ingeniería Biomédic